Intelligent Inspection Method of Hydropower Station Based on Artificial Intelligence and Internet of Things

In the inspection of hydropower stations, the inspection quality is low when only the inspection time factor is considered. Therefore, an intelligent inspection method for hydropower stations based on artificial intelligence and the Internet of Things is proposed. Based on the Internet of Things technology, the equipment operating parameters are collected, and the hydropower station operation information transmission chain is designed to transmit information between nodes and finally transmitted back to the computer of the monitoring center. The inspection problem is regarded as a multi-objective optimization problem, and the inspection time, cost, equipment importance and other factors are comprehensively considered to plan the inspection path of the hydropower station. The key features are extracted by artificial intelligence algorithm, and the hydropower station inspection model is constructed. The training is carried out by minimizing the loss function and continuously optimized. The objective function and variables are clarified, coded, and the individual fitness of the inspection path is evaluated. When the environmental influencing factors are large, the environmental factor grid processing method is used to eliminate the influence. The parameter over-limit detection is performed based on the discrete grid to realize the intelligent inspection of the hydropower station status. The experimental results show that in the detection of electrical fault vibration coupling waveforms, the design method is highly consistent with the actual monitoring waveform. In terms of the inspection coverage of the key areas of the hydropower station, the design method initially reaches 100%, and it still has 83% at 300 m, with a small decline, stable inspection capability, and higher inspection quality.